Predictive value of residual cholesterol inflammatory index for left atrial thrombus or spontaneous echo contrast in patients with nonvalvular atrial fibrillation with low CHA 2 DS 2 -VASc scores

Predictive value of residual cholesterol inflammatory index for left atrial thrombus or spontaneous echo contrast in patients with nonvalvular atrial fibrillation with low CHA 2 DS 2 -VASc scores | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive value of residual cholesterol inflammatory index for left atrial thrombus or spontaneous echo contrast in patients with nonvalvular atrial fibrillation with low CHA 2 DS 2 -VASc scores Yaqiong Jin, Li Wang, Yunmeng Wang, Jingchao Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7452942/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Currently, clinical guidelines are controversial regarding anticoagulation in patients with nonvalvular atrial fibrillation (NVAF) with low CHA 2 DS 2 -VASc scores (0–1 in men and 1–2 in women). Although these patients have low CHA 2 DS 2 -VASc scores, they are still at risk for left atrial thrombus (LAT) or spontaneous echo contrast (SEC) and further thromboembolism. Studies have shown that residual cholesterol inflammatory index (RCII) can assess both residual cholesterol and low-grade inflammation and is associated with thromboembolism, but the relationship between RCII and LAT/SEC in patients with NVAF has not been clear. Therefore, this study aimed to evaluate the predictive power of RCII for the occurrence of LAT/SEC in NVAF patients with low CHA 2 DS 2 -VASc scores. Methods All patients included in the study underwent transesophageal echocardiography (TEE). According to the results of TEE, the patients were divided into the LAT/SEC group and non-LAT /SEC group. The risk factors of LAT/SEC were analyzed by binary logistic regression. The correlation factors were combined with the CHA 2 DS 2 -VASc scores to develop a new prediction model for LAT/SEC, and the predictive efficacy of each model for LAT/SEC was further evaluated by using receiver operating characteristic (ROC). Results A total of 967 patients with NVAF were included in the study. The RCII level in the LAT/SEC group was significantly higher than that in the non-LAT /SEC group. Increased RCII levels and increased left atrial diameter (LAD) were independent risk factors for the development of LAT/SEC. The incidence of LAT/SEC was higher in the highest quartile array of RCII (> 8.81) and LAD (> 39mm) than in the corresponding lowest quartile array. The CHA 2 DS 2 -VASc scores combined with RCII and LAD have good predictive power for LAT/SEC. Conclusions For NVAF patients with low CHA 2 DS 2 -VASc scores, increased RCII levels and enlarged LAD are risk factors for LAT/SEC. The CHA 2 DS 2 -VASc scores combined with RCII and LAD significantly improved the predictive power of LAT/SEC. Nonvalvular atrial fibrillation Left atrial thrombus Spontaneous echo contrast Residual cholesterol inflammatory index Transesophageal echocardiography Figures Figure 1 Figure 2 Figure 3 Introduction Atrial fibrillation (AF) is the most common persistent arrhythmia in clinical practice, and its incidence is increasing with the improvement of chronic disease survival rate and population aging [ 1 ]. Left atrial thrombosis (LAT) is significantly associated with stroke in patients with nonvalvular atrial fibrillation (NVAF). Early assessment of stroke risk and timely anticoagulant therapy are critical to reduce thromboembolic events and mortality [ 2 ]. In addition, spontaneous echo contrast (SEC) in patients with AF indicates the pre-thrombotic state, which can further thrombosis and is therefore an indication of anticoagulation therapy [ 3 ]. At present, the CHA 2 DS 2 -VASc scores are mainly used in clinical practice to assess stroke risk in patients with AF, and anticoagulation therapy is guided according to the score [ 4 ]. Current clinical guidelines recommend anticoagulation therapy for AF patients with high CHA 2 DS 2 -VASc scores, while there is still debate about whether to give anticoagulant therapy to patients with low CHA 2 DS 2 -VASc scores (women:1–2 points; men:0–1 point), who are therefore at risk for LAT/SEC and thromboembolism [ 5 , 6 ]. Residual cholesterol (RC) is a triglyceride-rich lipoprotein cholesterol composed of very low-density lipoprotein (VLDL), medium-density lipoprotein (IDL), and chylomicron residues [ 7 ]. It is not only closely related to the occurrence and development of atherosclerosis but also a risk factor for hypertension, aortic stenosis, stroke, and death from cardiovascular disease [ 8 – 11 ]. RC can be deposited in the lining of blood vessels, leading to endothelial dysfunction and vascular inflammation. In addition, the triglycerides in RC can be broken down into free fatty acids and monoacylglycerol, thus aggravating the body's inflammatory response [ 12 , 13 ]. Studies have shown that elevated levels of RC and highly sensitive C-reactive protein (hs-CRP) can reflect low-grade inflammation in the body [ 14 , 15 ]. Elevated RC levels, combined with persistent low-grade inflammation, may promote the development of LAT/SEC in patients with NVAF and further influence stroke development. Residual cholesterol inflammatory index (RCII), calculated by RC and hs-CRP, provides a comprehensive assessment of residual cholesterol and low-grade inflammation [ 16 ]. Therefore, the purpose of this study was to evaluate the predictive ability of RCII for LAT/SEC in patients with NVAF with low CHA 2 DS 2 -VASc score, to identify patients whose CHA 2 DS 2 -VASc scores failed to detect thrombosis, give timely anticoagulant therapy, and improve their prognosis. Methods Study design and population This study was a single-center retrospective cohort study. This study collected data from 2681 patients with NVAF through the inpatient electronic medical record system, all of whom were admitted to the cardiology Department of the Second Hospital of Hebei Medical University between January 2022 and December 2023 and underwent transesophageal echocardiography (TEE) and transthoracic echocardiography (TTE). Inclusion criteria: 1) age>18 years old; 2) transthoracic and transesophageal echocardiography were completed, and the relevant clinical data were complete; 3) non-valvular atrial fibrillation. Exclusion criteria: 1) patients with high CHA 2 DS 2 -VASc scores (women≥3; men≥2); 2) patients with heart valve disease or who have previously undergone valve replacement or remodeling surgery; 3) patients with congenital heart disease; 4) Patients with cardiomyopathy; 5) patients with autoimmune diseases, hyperthyroidism, and other systemic diseases; 6) patients with severe hepatic and renal insufficiency or malignant tumor; 7) complicated with acute myocardial infarction or acute heart failure; 8) patients with incomplete clinical data. Eventually, 967 patients were enrolled in the study. All relevant information, including general clinical data, echocardiogram results, and laboratory test results, was collected from the electronic medical record system. Fig 1 shows the flow chart of the study. Ethical approval was obtained from the Ethics Committee of The Second Hospital of Hebei Medical University. The research was conducted according to the Helsinki Declaration guidelines. The diagnosis of AF is based on the ECG characteristics of AF on the routine 12 lead electrocardiograms, the persistent event of atrial fibrillation > 30s on the 24-hour Holter electrocardiogram, or the presence of a previous episode of AF. Hypertension is defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, or the use of antihypertensive drugs. To diagnose diabetes, fasting serum glucose levels of at least 7.0 mmol/L and/or random glucose levels of at least 11.1 mmol/L were required. Congestive heart failure is diagnosed based on characteristic symptoms and subsequently confirmed by a physician’s diagnosis. Peripheral artery disease (PAD) was diagnosed using vascular Doppler ultrasound or past medical history. The diagnosis of ischemic stroke is based on imaging evidence or ischemic stroke history. The diagnosis of coronary heart disease is based on relevant clinical guidelines or coronary heart disease history. Transthoracic echocardiography and transesophageal echocardiography were examined and measured by experienced senior sonographers, in which the left ventricular ejection fraction (LVEF) was measured by the modified Simpson method, and the left atrial diameter (LAD) was measured by the anterior and posterior diameters of the left atrium [17]. RC (mg/dL) was calculated as: RC=TC-(HDL-C+LDL-C). RCII was calculated by multiplying RC by hs-CRP, RCII=RC(mg/dL)×hs-CRP(mg/L)/10 [16]. CHA2DS2-VASc scores Based on the collected clinical information, the CHA 2 DS 2 -VASc scores were recalculated. 1 point is assigned for each risk variable, including congestive heart failure or left ventricular dysfunction, hypertension, diabetes, and vascular disease. Patients aged 65-74 years scored 1 point, and patients aged ≥75 years scored 2 points. Females get an extra point. 2 for stroke or transient ischemic attack. Low CHA 2 DS 2 -VASc scores included scores of 1-2 in female patients and 0-1 in male patients [18]. Echocardiographic examination All patients underwent transesophageal echocardiography to determine the presence of LAT or SEC. The diagnostic criteria for LAT are mobile, independent, round, or irregular in shape, uniform in density but different from that of the surrounding myocardial tissue, and can be detected in multiple parts of the left atrial lumen [19]. The diagnostic criteria for SEC are smoke, swirl, or pre-thrombotic states in the left atrium, but are distinct from the illusion caused by high-gain and near-field artifact changes [20]. Data related to cardiac cavity size and ventricular wall motion were collected by completing TTE. The echocardiogram is performed by two professional ultrasound physicians, one of whom is responsible for completing the procedure and making the diagnosis, while the other is responsible for reviewing the results. Neither doctor was aware of the patient's clinical condition before the examination. Statistical analyses Statistical analysis was performed using SPSS (Version 26.0, SPSS Inc., Chicago, IL, USA). Categorical variables were compared between groups using the χ 2 test and expressed as numbers (%). Continuous variables were first tested for normality by Kolmogorov-Smirnov, continuous variables with normal distribution were analyzed by t-test and expressed as mean ± standard deviation and continuous variables with non-normal distribution were analyzed by Mann-Whitney U test. Logistic regression analysis was used to explore the influencing factors of LAT/SEC formation in patients with NVAF. The predictive power of RCII and other risk variables was analyzed by mapping receiver operating characteristics (ROC). With two-sided P <0.05, the difference was considered to be statistically significant. Results Characteristics of the study population A total of 967 patients with NVAF were enrolled, with a mean age of 55.82 ± 9.81 years. According to the results of transesophageal echocardiography, they were divided into the non-LAT /SEC group (n = 849) and the LAT/SEC group (n = 118). In the LAT/SEC group, 75 patients developed LAT, of which 25 patients combined with SEC and only 43 patients developed SEC. The incidence of LAT and SEC accounted for 7.76% and 4.45% of the population, respectively. As shown in Table 1 , heart failure (HF) (16.95% vs. 2.24%) was more prevalent in the LAT/SEC group, with higher body mass index (BMI) levels [27.12 ± 3.77 kg/m 2 vs. 26.15 ± 3.67 kg/m 2 , P < 0.01], total cholesterol (TC) levels[181.72 (156.70-208.29) mg/dL vs. 166.32 (145.15-190.19) mg/dL, P < 0.01], and lipoprotein(a) (Lp(a)) levels [15.63 (7.09–26.13) mg/L vs. 11.91 (6.11–23.42) mg/L, P = 0.023]compared to the non-LAT /SEC group. In addition, serum uric acid (SUA) [367.10 ± 90.81 umol/L vs. 340.83 ± 94.38 umol/L, P < 0.005], aspartate aminotransferase (AST) [20.00 (16.30–26.00) U/L vs. 18.65 (15.20–23.00) U/L, P = 0.006], high-sensitivity C-reactive protein (hs-CRP) [2.00 (1.48–4.48) mg/L vs. 1.70 (1.00-3.98) mg/L, P < 0.01], monocyte count [0.60 (0.43–0.80)×10 9 /L vs. 0.54 (0.41–0.70)×10 9 /L, P = 0.021], red blood cell count (RBC) [4.67 (4.38–5.03) g/L vs. 4.54 (4.23–4.89) g/L, P = 0.004], red cell distribution width standard deviation (RDWSD) [43.80 (42.00-45.03) fl vs. 42.90 (41.10–44.60) fl, P = 0.017], and residual cholesterol inflammatory index (RCII) [9.04 (5.18–16.90) vs. 3.81 (2.00-7.84), P < 0.01] were also higher in this group. Left ventricular ejection fraction (LVEF) [60.00 (51.75–61.70) % vs. 61.40 (59.10–65.40) %, P < 0.01], Lymphocyte count [1.75 ± 0.49×10 9 /L vs. 1.93 ± 0.63×10 9 /L, P = 0.003] and paroxysmal atrial fibrillation (AF) (55.90% vs. 88%) in LAT/SEC group were lower. No statistical differences were observed in terms of gender, age, systolic blood pressure, diastolic blood pressure, CHA 2 DS 2 -VASc scores, fasting plasma glucose (FPG), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum creatinine (SCr), alanine aminotransferase (ALT), white blood cell count (WBC), neutrophil count, platelet count (PLT), platelet distribution width (PDW), mean platelet volume (MPV), smoke, alcohol consumption, previous diabetes, ischemic stroke, peripheral vascular disease (PAD), hyperlipidemia, chronic kidney disease (CKD), and pre-hospitalization medication. Table.1. Baseline characteristics of NVAF patients with/without LAT/SEC. Variable Total (n = 967) Non-LAT/SEC (n = 849) LAT/SEC (n = 118) X 2 / Z / t P -value Male, n (%) 651(67.32) 562(66.20) 89(75.40) 3.602 0.058 Age (years old) 57.00 (51.00–62.00) 57.00 (51.00–62.00) 57.50 (53.00–62.00) -1.372 0.170 BMI (kg/m 2 ) 26.27 ± 3.69 26.15 ± 3.67 27.12 ± 3.77 2.676 0.008 * SBP (mmHg) 126(117–135) 126(117–135) 125(113–133) -1.322 0.186 DBP (mmHg) 80.00 (75.00–89.00) 80.00 (75.00–89.00) 80.00 (74.00–91.00) -0.149 0.881 CHA 2 DS 2 -VASc score 1(0–1) 1(0–1) 1(0–1) -0.074 0.941 LAD (mm) 37.00 (34.00–39.00) 37.00 (33.00–39.00) 39.50 (38.00–41.00) -8.370 <0.001 * LVEDD (mm) 47.00 (45.00–50.00) 47.00 (45.00–49.00) 47.00 (46.00-52.25) -2.775 0.007 * LVEF (%) 61.00 (58.50–65.00) 61.40 (59.10–65.40) 60.00 (51.75–61.70) -5.509 <0.001 * E/e' 10.29 (8.14–11.85) 10.19 (8.10-11.85) 11.45 (8.86–12.68) -2.525 0.012 * FPG (mmol/L) 4.98 (4.54–5.35) 4.99 (4.53–5.35) 4.92 (4.55–5.35) -0.340 0.734 TG (mg/dL) 108.98(80.63-159.48) 110.75 (80.63-160.37) 101.89 (78.85-147.96) -0.894 0.371 TC (mg/dL) 167.09 (146.69-192.12) 166.32 (145.15-190.19) 181.72 (156.70-208.29) -3.505 <0.001 * HDL-C(mg/dL) 42.57 (35.22–48.38) 42.57 (35.60-48.38) 41.41 (34.44–48.38) -0.458 0.647 LDL-C(mg/dL) 101.01 ± 30.57 101.39 ± 30.57 97.52 ± 30.96 -1.358 0.175 Lp(a) (mg/L) 12.48 (6.31–24.19) 11.91 (6.11–23.42) 15.63 (7.09–26.13) -2.274 0.023 * SCr (umol/L) 72.00 (61.00–82.00) 72.00 (61.00–82.00) 73.00 (61.00–82.00) -0.409 0.683 SUA (umol/L) 344.03 ± 94.30 340.83 ± 94.38 367.10 ± 90.81 2.845 0.005 * ALT (U/L) 20.70 (14.25–28.95) 20.25 (14.00-28.60) 21.40 (16.15–29.90) -1.885 0.059 AST (U/L) 19.00 (15.30–23.00) 18.65 (15.20–23.00) 20.00 (16.30–26.00) -2.740 0.006 * hs-CRP (mg/L) 1.80 (1.00–4.00) 1.70 (1.00-3.98) 2.00 (1.48–4.48) -3.579 <0.001 * WBC (10 9 /L) 5.90 (4.90–6.96) 5.90 (4.90–6.92) 5.72 (4.91–7.22) -0.211 0.833 Neutrophil count (10 9 /L) 3.38 (2.67–4.29) 3.37 (2.63–4.27) 3.40 (2.72–4.40) -0.471 0.638 Lymphocyte count (10 9 /L) 1.91 ± 0.62 1.93 ± 0.63 1.75 ± 0.49 -2.947 0.003 * Monocyte count (10 9 /L) 0.55 (0.42–0.70) 0.54 (0.41–0.70) 0.60 (0.43–0.80) -2.310 0.021 * RBC (g/L) 4.57 (4.25–4.91) 4.54 (4.23–4.89) 4.67 (4.38–5.03) -2.845 0.004 * RDWSD (fl) 42.90 (41.30–44.60) 42.90 (41.10–44.60) 43.80 (42.00-45.03) -2.377 0.017 * PLT (10 9 /L) 208.00 (175.00-244.00) 209.40 (177.00-246.00) 201.00 (169-233.75) -1.480 0.139 PDW (fl) 16.40 (13.10–16.90) 16.40 (13.10–16.90) 16.40 (13.65-17.00) -0.340 0.734 MPV (fl) 9.20 (8.30–10.20) 9.20 (8.30–10.20) 9.33 (8.50–10.20) -1.587 0.112 RCII 4.58 (2.23–8.81) 3.81 (2.00-7.84) 9.04 (5.18–16.90) -8.683 <0.001 * Paroxysmal AF, n (%) 813(84.07) 747 (88.00) 66 (55.90) 79.499 <0.001 * Smoke, n (%) 181 (18.72) 153 (18.02) 28 (23.73) 2.218 0.136 Alcohol consumption, n (%) 161 (16.65) 134 (15.78) 27 (22.88) 3.761 0.052 HF, n (%) 39 (4.03) 19 (2.24) 20 (16.95) 54.191 <0.001 * Hypertension, n (%) 327 (33.82) 291 (34.28) 36 (30.51) 0.657 0.418 Diabetes, n (%) 36 (3.72) 32 (3.77) 4 (3.39) 0.042 0.838 Ischemic stroke, n (%) 3(0.3) 2(0.2) 1(0.8) 0.056 0.813 PAD, n (%) 19 (1.96) 14 (1.65) 5 (4.24) 2.385 0.123 Hyperlipidemia, n (%) 140 (14.48) 129 (15.2) 11(9.3) 2.885 0.089 CKD, n (%) 11 (1.14) 11 (1.30) 0 (0.00) 0.609 0.435 Prehospital medication, n (%) Warfarin 14 (1.45) 12 (1.4) 2 (1.7) 0.058 0.810 NOAC 151(15.62) 134 (15.78) 17(14.41) 0.149 0.700 Antiplatelet agent 116 (12.00) 104 (12.25) 12 (10.17) 0.425 0.515 Statins 151 (15.62) 133 (15.7) 18 (15.3) 0.013 0.908 * P < 0.05. SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; LAD, left atrium diameter; LVEDD, left ventricular end-diastolic dimension; LVEF, left ventricular ejection fraction; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SCr, serum creatinine; SUA, serum uric acid; ALT, alanine aminotransferase; AST, aspartate aminotransferase; hs-CRP, high-sensitivity C-reactive protein; WBC, white blood cell count; RBC, red blood cell count; RDWSD, red cell distribution width standard deviation; PLT, platelet count; PDW, platelet distribution width; MPV, mean platelet volume; RCII, residual cholesterol inflammatory index; HF, heart failure; PAD, peripheral vascular disease; AF, atrial fibrillation; CKD, chronic kidney disease; NOAC, new oral anticoagulants; Lp(a), lipoprotein(a). Risk factors for LAT/SEC Variables with P < 0.05 in the baseline table were selected to be included in univariable logistics regression to determine the risk factors for the formation of LAT/SEC and multivariate logistics regression analysis was performed. The results showed that LAD, lipoprotein(a), hs-CRP, lymphocyte count, monocyte count, history of heart failure, non-paroxysmal atrial fibrillation, and RCII were all predictors of LAT/SEC ( Table 2 ). Subjects were divided into four groups according to the quartile of RCII and LAD levels. Multivariate logistic regression analysis showed that RCII level was correlated with LAT/SEC ( Table 3 ). Specifically, patients with the highest quartile array showed a higher incidence of LAT/SEC compared to the lowest quartile array. After adjusting for age and gender in Model I and other confounding factors including gender, age, body mass index, left ventricular ejection fraction, left ventricular end-diastolic dimension, E/e', total cholesterol, lipoprotein(a), serum uric acid, alanine aminotransferase, aspartate aminotransferase, high-sensitivity C-reactive protein, heart failure, and paroxysmal atrial fibrillation in Model II, the association between RCII and LAT/SEC formation remained consistent. Regardless of adjusting for confounding factors, the incidence of LAT/SEC increased significantly with the enlargement of the left atrium. As shown in Fig. 2 , the ROC curve was drawn to analyze the prediction efficiency of RCII and LAD for LAT/SEC. The results showed that the area under the curve (AUC) of RCII was 0.746 (95% CI 0.705–0.787, P < 0.01), while that of LAD was 0.736 (95% CI 0.707–0.764, P < 0.01). Table.2. Univariable and multivariate logistic regression for risk factors of LAT/SEC. Variable Univariable Multivariate OR 95%CI P -value OR 95%CI P -value BMI 1.072 1.018–1.129 0.008 * 1.067 1.000-1.138 0.050 LAD 1.180 1.130–1.231 <0.001 * 1.100 1.034–1.169 0.003 * LVEDD 1.093 1.050–1.137 <0.001 * 1.007 0.952–1.065 0.810 LVEF 0.929 0.908–0.951 <0.001 * 0.999 0.962–1.036 0.938 E/e' 1.080 1.027–1.136 0.003 * 0.984 0.922–1.050 0.632 TC 1.010 1.005–1.016 <0.001 * 0.999 0.992–1.007 0.839 Lp(a) 1.012 1.004–1.020 0.003 * 1.012 1.001–1.023 0.031 * SUA 1.003 1.001–1.005 0.005 * 1.001 0.998–1.003 0.627 AST 1.002 0.996–1.008 0.562 - - - hs-CRP 1.060 1.017–1.104 0.006 * 0.552 0.457–0.666 <0.001 * Lymphocyte count 0.596 0.422–0.842 0.003 * 0.584 0.375–0.909 0.017 * Monocyte count 4.067 2.075–7.970 <0.001 * 4.238 1.753–10.241 0.001 * RBC 1.792 1.230–2.611 0.002 * 1.533 0.905–2.599 0.112 RDWSD 1.067 1.012–1.125 0.017 * 1.046 0.977–1.121 0.194 RCII 1.049 1.033–1.066 <0.001 * 1.232 1.159–1.309 <0.001 * Paroxysmal AF 0.173 0.114–0.263 <0.001 * 0.259 0.153–0.439 <0.001 * HF 8.915 4.599–17.281 <0.001 * 3.178 1.240–8.150 0.016 * * P < 0.05. BMI, body mass index; LAD, left atrium diameter; LVEDD, left ventricular end-diastolic dimension; LVEF, left ventricular ejection fraction; TC, total cholesterol; SUA, serum uric acid; ALT, alanine aminotransferase; AST, aspartate aminotransferase; hs-CRP, high-sensitivity C-reactive protein; RBC, red blood cell count; RDWSD, red cell distribution width standard deviation; RCII, residual cholesterol inflammatory index; HF, heart failure; AF, atrial fibrillation; Lp(a), lipoprotein(a); LAT/SEC left atrial thrombosis/spontaneous echo contrast. Table.3. Univariable and multivariate logistic regression for risk factors of LAT/SEC. Variable Crude Model I Model II OR (95%CI) P OR (95%CI) P OR (95%CI) P RCII 1.05 (1.03–1.07) <0.001 * 1.04 (1.03–1.06) <0.001 * 1.04 (1.02–1.06) <0.001 * Q1 ref. ref. ref. ref. ref. ref. Q2 7.96 (1.80-35.22) <0.006 * 6.53 (1.46–29.14) <0.014 * 7.98 (1.55–41.04) 0.013 * Q3 23.88 (5.70-100.03) <0.001 * 20.82 (4.92–88.11) <0.001 * 23.21 (4.61-116.92) <0.001 * Q4 40.22 (9.71-166.64) <0.001 * 31.86 (7.62-133.17) <0.001 * 18.93 (3.47-103.23) <0.001 * LAD 1.18 (1.13–1.23) <0.001 * 1.17 (1.12–1.22) <0.001 * 1.10 (1.04–1.17) <0.001 * Q1 ref. ref. ref. ref. ref. ref. Q2 6.89 (2.28–20.81) <0.001 * 6.85 (2.24–20.96) <0.001 * 7.11 (1.55–41.04) 0.003 Q3 13.47 (4.73–38.31) <0.001 * 11.58 (4.01–33.44) <0.001 * 11.78 (3.37–41.20) <0.001 * Q4 25.19 (8.99–70.56) <0.001 * 20.75 (7.31–58.95) <0.001 * 12.71 (3.52–45.89) <0.001 * * P < 0.05. RCII, residual cholesterol inflammatory index; LAD left atrial diameter; LAT/SEC, left atrial thrombosis/spontaneous echo contrast; OR, odds ratio; CI, confidence interval. Model I adjusted for gender and age. Mode II adjusted for gender, age, body mass index, left ventricular ejection fraction, left ventricular end-diastolic dimension, E/e', total cholesterol, lipoprotein(a), serum uric acid, alanine aminotransferase, aspartate aminotransferase, high-sensitivity C-reactive protein, heart failure, and paroxysmal atrial fibrillation. For the RCII group: Q1: RCII ≤ 2.23; Q2: 2.23 < RCII ≤ 4.58; Q3: 4.58 8.81. For the LAD group: Q1: LAD ≤ 34 mm; Q2: 34 < LAD ≤ 37mm; Q3: 37 39 mm. RCII and LAD enhance the prediction accuracy of the CHADS‑VASc score Increased RCII and left atrial enlargement are risk factors for LAT/SEC formation. RCII and LAD were incorporated into the new forecast model. As shown in Table 4 and Fig. 3 , the CHA 2 DS 2 -VASc scores had poor predictive power for LAT/SEC (AUC = 0.502, P > 0.05). After the inclusion of RCII (Model A), the prediction efficiency was significantly improved (AUC difference = 0.248, Z = 7.350, P < 0.01). After combining RCII and LAD (Model B), the model prediction efficiency was improved (AUC difference = 0.285, Z = 8.589, P < 0.01). The prediction efficiency of Model B was higher than that of Model A, and the difference was statistically significant (AUC difference = 0.037, Z = 2.061, P = 0.039). Table.4. Comparison between the prediction models. Variable AUC 95%CI P † Z P ‡ CHA 2 DS 2 -VASc score 0.502 0.470–0.534 0.940 0.075 - CHA 2 DS 2 -VASc score + RCII 0.750 0.709–0.791 <0.001 7.350 * <0.001 * CHA 2 DS 2 -VASc score + RCII + LAD 0.787 0.748–0.826 <0.001 8.589 * <0.001 * 2.061 ** 0.039 ** † P -value for each ROC curve analysis ‡ The P -value when comparing between the two models * Compared to the CHA 2 DS 2 -VASc score group ** Compared to the CHA 2 DS 2 -VASc score + RCII group AUC, area under the receiver operating characteristic curve, CI, confidence interval, LAD, left atrial diameter, RCII, residual cholesterol inflammatory index. Discussion Atrial fibrillation (AF) is the most common arrhythmia, and because there are usually no obvious symptoms, it significantly increases the risk of stroke and all-cause death [ 21 ]. Studies have shown that SEC and LAT are significantly correlated with AF-related embolism [ 22 ]. Early identification of high-risk patients and timely anticoagulation therapy are of great significance for improving patient prognosis. Current clinical guidelines recommend anticoagulation therapy for patients with high CHA 2 DS 2 -VASc scores, while there is still controversy over whether patients with low CHA 2 DS 2 -VASc scores (0–1 in men and 1–2 in women) should be treated with anticoagulation [ 18 ]. Many studies have shown that the CHA 2 DS 2 -VASc scores have limited predictive power in clinical practice, especially for patients with low thrombosis scores and poor sensitivity to LAT [ 23 , 24 ]. Previous studies have shown that patients with NVAF with low CHA 2 DS 2 -VASc scores will still have thromboembolic events, among which the annual stroke rate of women with a score of 2 and men with a score of 1 is about 3%, while the incidence of thrombotic events is as high as 11.4% in patients with a score of 0–1 [ 25 , 26 ]. However, the CHA 2 DS 2 -VASc scores may be delayed to some extent for low-risk patients without previous embolic events only after the occurrence of embolic events, and this scoring system mainly takes stroke as the main outcome event rather than left atrial thrombosis [ 27 ]. In patients with NVAF, LAT may occur when atrial fibrillation lasts longer than 48 hours, and SEC is an important marker of thrombosis [ 28 ]. LAT and SEC were independently associated with thromboembolic events in patients with atrial fibrillation [ 29 ]. Previous studies have shown that the average incidence of LAT in patients with NVAF is about 9.8% [ 30 ]. Another study showed that the incidence of LAT and SEC in 481 NVAF patients was 12.47% and 11.43%, respectively [ 27 ]. However, the incidence rates of SEC and LAT in this study were 4.45% and 7.76%, respectively, lower than those reported in previous studies. This difference between the results of the studies may be related to demographic differences in the included populations and potential selection bias in the studies. Therefore, this study was designed to evaluate patients at high risk of thromboembolism with low CHA 2 DS 2 -VASc scores, to provide references for clinical treatment to improve patient outcomes. Residual cholesterol (RC) refers to the cholesterol content in all triglyceride-rich lipoprotein (TRL), including the sum of the cholesterol in fasting very low-density lipoprotein (VLDL) and intermediate density lipoprotein (IDL) and the cholesterol in non-fasting chylomicron [ 31 ]. It is calculated by subtracting high-density lipoprotein cholesterol (HDL-C) and high-density lipoprotein cholesterol (LDL-C) from total cholesterol (TC) [ 32 ]. Recent studies have found that residual cholesterol (RC) is associated with an increased risk of cardiovascular diseases such as myocardial infarction, atrial fibrillation, and ischemic stroke [ 33 – 35 ]. Results from a genetic study show that RC has a greater effect on cardiovascular risk than LDL-C [ 36 ]. Compared with LDL-C, RC can carry more cholesterol and is more easily captured by macrophages, so RC has a stronger ability to cause arteriosclerosis [ 37 ]. In addition, RC can promote the expression of cytokines interleukin and other inflammatory mediators, leading to chronic low-grade inflammation, and then promote the formation and progression of atherosclerosis [ 38 ]. Another study showed that in the LDL-C quartile group, the highest group had a significantly lower cumulative incidence of atrial fibrillation than the lowest group [ 39 ]. However, there is currently a cholesterol paradox and there is still little evidence for a relationship between RC and thromboembolic risk in AF, so this study aimed to evaluate the relationship between RC and LAT/SEC in NVAF. Atrial remodeling caused by inflammatory states is the basis for the occurrence and maintenance of atrial fibrillation [ 40 ]. C-reactive protein (CRP), as a commonly used clinical indicator of inflammation, is associated with the occurrence of AF [ 41 ]. Previous studies have shown that CRP is slightly elevated in patients with AF, which may reflect the chronic inflammatory state of the body to a certain extent, which can lead to the remodeling of the heart and blood vessels, and thus promote the occurrence of AF [ 42 ]. In addition, previous studies have shown that elevated CRP increases the risk of atrial myocyte calcium influx [ 43 ]. A retrospective study showed that IL-6, highly sensitive C-reactive protein (hs-CRP), and white blood cell count were positively correlated with the occurrence of AF in elderly patients [ 44 ]. CRP as an inflammatory marker, can predict the recurrence of AF after electrocardioversion [ 45 ]. The Residual Cholesterol Inflammation Index (RCII), calculated by RC and hs-CRP, provides a comprehensive assessment of residual cholesterol and low-grade inflammation. Therefore, this study aimed to evaluate the predictive power of RCII for LAT/SEC in NVAF patients with low CHA2DS2-VASc scores. The study structure shows that RCII has a good predictive ability for LAT/SEC, the area under the curve is 0.746, and the optimal critical value is 4.46. In this study, in addition to RCII, left atrial diameter (LAD) was also found to be a predictor of LAT/SEC. Changes in atrial structure, such as atrial cardiomyocyte hypertrophy and fibrosis, and atrial dilation, can lead to shortened action potential, reduced electrical connections between cells, and altered Ca 2+ processing [ 46 ]. This study showed a significant increase in LAD in the LAT/SEC group compared to the non-LAT /SEC group (39.50 (38.00–41.00) vs. 37.00 (33.00–39.00), P < 0.001). After combining RCII and LAD, the ability of the CHA 2 DS 2 -VASc scores to predict LAT/SEC was significantly improved (AUC difference = 285, Z = 8.589, P < 0.001). Therefore, in assessing the risk of thrombosis in NVAF patients with low CHA 2 DS 2 -VASc scores, when RCII ≥ 4.46 and LAD ≥ 36.5 mm indicate an increased likelihood of LAT formation, TEE should be actively performed to detect intra-atrial thrombosis. At present, there are few studies on the incidence of LAT/SEC in NVAF patients with low CHA 2 DS 2 -VASc scores. This study was the first to establish the predictive efficacy of RCII for the occurrence of LAT/SEC in patients with NVAF, with an optimal cut-off value of 4.46. In addition, left atrial enlargement was found to be an independent risk factor for LAT/SEC, which was consistent with previous studies. This study found that the combination of RCII, LAD, and CHA 2 DS 2 -VASc scores could significantly enhance the prediction efficiency of LAT/SEC. Therefore, RCII and LAD should be combined to assess thromboembolic risk in NVAF patients with low CHA 2 DS 2 -VASc scores. This helps to assess and identify patients with potential thrombosis as early as possible, perform TEE examination promptly, and administer anticoagulant therapy to improve patient outcomes. However, there are some limitations in this study. First of all, this study is a single-center retrospective study, which may be biased. In addition, the relevant clinical data collected in this study were limited, only LAD was used to evaluate left atrial structure, and no other indicators of left atrial function were collected. Therefore, whether RCII can be used in the clinical assessment of patients' thrombosis risk remains to be further confirmed by large-scale prospective studies. Conclusions In conclusion, increased RCII and LAD were independent risk factors for LAT/SEC in NVAF patients with low CHA 2 DS 2 -VASc scores. CHA 2 DS 2 -VASc scores combined with RCII and LAD can significantly improve its prediction efficiency. Therefore, RCII, LAD, and CHA 2 DS 2 -VASc scores should be combined for comprehensive analysis in assessing thromboembolism risk in patients with NVAF. Declarations Funding Not applicable. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethical approval and consent to participate All methods were carried out in accordance with the Declaration of Helsinki. The Ethics Review Committee of the second hospital of Hebei medical university approved the study and written informed consents were obtained from all participants. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Disclosure statement No potential conflict of interest was reported by the author(s). Clinical trial number Not applicable. References Kornej J, Börschel CS, Benjamin EJ, et al. Epidemiology of Atrial Fibrillation in the 21st Century: Novel Methods and New Insights. Circ Res. 2020;127(1):4-20. Jame S, Barnes G. Stroke and thromboembolism prevention in atrial fibrillation. Heart. 2020;106(1):10-17. Shi B, Suo R, Song W, et al. Plasma metabolomic characteristics of atrial fibrillation patients with spontaneous echo contrast. BMC Cardiovasc Disord. 2024;24(1):654. Joglar JA, Chung MK, Armbruster AL, et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149(1):e167. Hao L, Chen X, Sun W, et al. Risk Factors for Silent Brain Infarction in Nonvalvular Atrial Fibrillation Patients with Low CHA 2 DS 2 -VASc Score. Curr Neurovasc Res. 2024. Kim YK, Lee SR, Choi EK, et al. Risk of Death From Various Causes According to Prevalent Atrial Fibrillation: A Nationwide Population-Based Study. J Korean Med Sci. 2024;39(47):e306. Ouyang X, Tang X, Peng L, et al. Remnant cholesterol and new-onset atrial fibrillation: The Atherosclerosis Risk in Communities study. Heart Rhythm. 2024;S1547-5271(24)03456-3458. Zhang K, Qi X, Zhu F, et al. Remnant cholesterol is associated with cardiovascular mortality. Front Cardiovasc Med. 2022;9:984711. Navarese EP, Vine D, Proctor S, et al. Independent Causal Effect of Remnant Cholesterol on Atherosclerotic Cardiovascular Outcomes: A Mendelian Randomization Study. Arterioscler Thromb Vasc Biol. 2023;43(9):e373-e380. Li ZH, Hao QY, Zeng YH, et al. Remnant cholesterol and the risk of aortic valve calcium progression: insights from the MESA study. Cardiovasc Diabetol. 2024;23(1):20. Guo DC, Gao JW, Wang X, et al. Remnant cholesterol and risk of incident hypertension: a population-based prospective cohort study. Hypertens Res. 2024;47(5):1157-1166. Van Diepen JA, Berbée JF, Havekes LM, et al. Interactions between inflammation and lipid metabolism: relevance for efficacy of anti-inflammatory drugs in the treatment of atherosclerosis. Atherosclerosis. 2013;228(2):306-315. Chait A, Ginsberg HN, Vaisar T, et al. Remnants of the Triglyceride-Rich Lipoproteins, Diabetes, and Cardiovascular Disease. Diabetes. 2020;69(4):508-516. Wadström BN, Pedersen KM, Wulff AB, et al. Elevated remnant cholesterol, plasma triglycerides, and cardiovascular and non-cardiovascular mortality. Eur Heart J. 2023;44(16):1432-1445. Small AM, Pournamdari A, Melloni GEM, et al. Lipoprotein(a), C-Reactive Protein, and Cardiovascular Risk in Primary and Secondary Prevention Populations. JAMA Cardiol. 2024;9(4):385-391. Chen J, Wu Q, Liu H, et al. Predictive value of remnant cholesterol inflammatory index for stroke risk: Evidence from the China health and Retirement Longitudinal study. J Adv Res. 2024. 2024;S2090-1232(24)00592-597. Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging. 2015;16(3):233-270. Hindricks G, Potpara T, Dagres N, et al. Corrigendum to: 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J. 2021;42(40):4194. Koca V, Bozat T, Akkaya V, et al. Left atrial thrombus detection with multiplane transesophageal echocardiography: an echocardiographic study with surgical verification. J Heart Valve Dis. 1999;8(1):63-66. Black IW, Hopkins AP, Lee LC, et al. Left atrial spontaneous echo contrast: a clinical and echocardiographic analysis. J Am Coll Cardiol. 1991;18(2):398-404. Yildirim M, Milles BR, Hund H, et al. Outcomes and Disease Management in Patients With Atrial Fibrillation ≥80 Years: Data From a Consecutive 11-Year Real-World Registry. J Am Heart Assoc. 2025;14(4):e036832. Li Z, Pan L, Deng Y, et al. Development and Validation of a Nomogram for Estimation of Left Atrial Thrombus or Spontaneous Echo Contrast Risk in Non-Valvular Atrial Fibrillation Patients with Low to Borderline CHA 2 DS 2 -VASc Score. Int J Gen Med. 2022;15:7329-7339. Angebrandt Belošević P, Šmalcelj A, Kos N, et al. Left Ventricular Ejection Fraction Can Predict Atrial Thrombosis Even in Non-High-Risk Individuals with Atrial Fibrillation. J Clin Med. 2022;11(14):3965. Chen J, Zhou M, Wang H, et al. Risk factors for left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients with low CHA 2 DS 2 -VASc score. J Thromb Thrombolysis. 2022;53(2):523-531. Yan S, Li Q, Xia Z, et al. Risk factors of thromboembolism in nonvalvular atrial fibrillation patients with low CHA 2 DS 2 -VASc score. Medicine (Baltimore). 2019;98(8):e14549. Xie X, Li T. Anticoagulation for atrial fibrillation patients with the CHA 2 DS 2 -VASc score =1 (beyond sex). Acta Cardiol. 2021;76(3):258-264. Kamili K, Zheng T, Luo C, et al. Predictive value of lipoprotein(a) for left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients with low CHA 2 DS 2 -VASc scores: a cross-sectional study. Lipids Health Dis. 2024;23(1):22. Kim YG, Shim J, Oh SK, et al. Risk Factors for Ischemic Stroke in Atrial Fibrillation Patients Undergoing Radiofrequency Catheter Ablation. Sci Rep. 2019;9(1):7051. Yoo J, Song D, Baek JH, et al. Poor Outcome of Stroke Patients With Atrial Fibrillation in the Presence of Coexisting Spontaneous Echo Contrast. Stroke. 2016;47(7):1920-1922. Di Minno MN, Ambrosino P, Dello Russo A, et al. Prevalence of left atrial thrombus in patients with non-valvular atrial fibrillation. A systematic review and meta-analysis of the literature. Thromb Haemost. 2016;115(3):663-677. Makover ME, Surma S, Banach M, et al. Eliminating atherosclerotic cardiovascular disease residual risk. Eur Heart J. 2023;44(45):4731-4733. Mach F, Baigent C, Catapano AL, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41(1):111-188. Li W, Huang Z, Fang W, et al. Remnant Cholesterol Variability and Incident Ischemic Stroke in the General Population. Stroke. 2022;53(6):1934-1941. Jørgensen AB, Frikke-Schmidt R, West AS, et al. Genetically elevated non-fasting triglycerides and calculated remnant cholesterol as causal risk factors for myocardial infarction. Eur Heart J. 2013;34(24):1826-1833. Zhong L, Xie B, Wang HL, et al. Causal association between remnant cholesterol level and risk of cardiovascular diseases: a bidirectional two sample mendelian randomization study. Sci Rep. 2024;14(1):27038. Björnson E, Adiels M, Taskinen MR, et al. Triglyceride-rich lipoprotein remnants, low-density lipoproteins, and risk of coronary heart disease: a UK Biobank study. Eur Heart J. 2023;44(39):4186-4195. Castañer O, Pintó X, Subirana I, et al. Remnant Cholesterol, Not LDL Cholesterol, Is Associated With Incident Cardiovascular Disease. J Am Coll Cardiol. 2020;76(23):2712-2724. Baratta F, Cocomello N, Coronati M, et al. Cholesterol Remnants, Triglyceride-Rich Lipoproteins and Cardiovascular Risk. Int J Mol Sci. 2023;24(5):4268. Li L, Wang C, Ye Z, et al. Association Between Remnant Cholesterol and Risk of Incident Atrial Fibrillation: Population-Based Evidence From a Large-Scale Prospective Cohort Study. J Am Heart Assoc. 2024;13(10):e033840. Galvão Braga C, Ramos V, Vieira C, et al. New-onset atrial fibrillation during acute coronary syndromes: predictors and prognosis. Rev Port Cardiol. 2014;33(5):281-287. Sun GZ, Guo L, Wang J, et al. Association between hyperuricemia and atrial fibrillation in rural China: a cross-sectional study. BMC Cardiovasc Disord. 2015;15:98. Chung MK, Martin DO, Sprecher D, et al. C-reactive protein elevation in patients with atrial arrhythmias: inflammatory mechanisms and persistence of atrial fibrillation. Circulation. 2001;104(24):2886-2891. Ding M, Wennberg A, Gigante B, et al. Lipid levels in midlife and risk of atrial fibrillation over 3 decades-Experience from the Swedish AMORIS cohort: A cohort study. PLoS Med. 2022;19(8):e1004044. Shitole SG, Heckbert SR, Marcus GM, et al. Assessment of Inflammatory Biomarkers and Incident Atrial Fibrillation in Older Adults. J Am Heart Assoc. 2024;13(24):e035710. Sonsöz MR, Demirtaş İ, Canbolat O, et al High-density lipoprotein cholesterol to c-reactive protein ratio predicts atrial fibrillation recurrence after electrical cardioversion. Lipids. 2024. Zhou X, Dudley SC Jr. Evidence for Inflammation as a Driver of Atrial Fibrillation. Front Cardiovasc Med. 2020;7:62. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7452942","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530517408,"identity":"05e3952c-4ed0-46df-bb55-4d2c581af5d9","order_by":0,"name":"Yaqiong Jin","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaqiong","middleName":"","lastName":"Jin","suffix":""},{"id":530517410,"identity":"b36d9988-2ad2-44b4-98e9-b4607f5ae0a2","order_by":1,"name":"Li Wang","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Wang","suffix":""},{"id":530517411,"identity":"ee9697a9-ad61-4e55-b4e6-8207a3f40124","order_by":2,"name":"Yunmeng Wang","email":"","orcid":"","institution":"Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yunmeng","middleName":"","lastName":"Wang","suffix":""},{"id":530517412,"identity":"dc832dde-d094-44f7-a6fa-08783f4f0a50","order_by":3,"name":"Jingchao Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACPoYDbMwMFQfkDBh4wAKMDYS0sIG1nDlgTIoWBjZmxrYDiRuI18J4+NnjArY76dvZe49u5mGwkd1wgPnZA/y2HDM3nsHzLHdnz7m02zwMacYbDrCZG+DXcoZNmkficO6GGzlmQC2HEzcc4GGTIKzF4HC6wf03IC3/idWScDjB4AYPSMsBYrQcM5OeceCw4YYzeWk35xgkG888zGaGVwu/xOFn0oX/DssbHD977MabCjvZvuPNz/BqYZA4gMwDBRUzXvUgaxoIqRgFo2AUjIIRDwDEYkw6h7KwSgAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jingchao","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2025-08-25 10:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7452942/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7452942/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93883268,"identity":"e1ed44e1-e735-4c34-b0f8-cf646df27c99","added_by":"auto","created_at":"2025-10-19 16:59:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":296627,"visible":true,"origin":"","legend":"","description":"","filename":"MainManuscriptrevised.docx","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/fa7789d6a5cf36f8b891e390.docx"},{"id":93881959,"identity":"2020cbf9-550e-4ade-a5c7-96c3ba6844df","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6510,"visible":true,"origin":"","legend":"","description":"","filename":"39fc0530c814408a97325755691cf5d3.json","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/b231e74f24031d727f9e527c.json"},{"id":93881961,"identity":"78c02703-18fe-430b-96d0-4f2a71bef97c","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149576,"visible":true,"origin":"","legend":"","description":"","filename":"39fc0530c814408a97325755691cf5d31enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/287b35b271ed6b438d6d9f92.xml"},{"id":93881969,"identity":"10be3719-fb7d-493f-98bd-d9453f9ecb70","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"eps","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103709,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage1.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/578dca95ef635a7f1f28204b.eps"},{"id":93881958,"identity":"68c0e231-db4f-46c9-93aa-171476f16e70","added_by":"auto","created_at":"2025-10-19 16:51:56","extension":"eps","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":372,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage10.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/7d4df0bde83f2d010cb5e2a1.eps"},{"id":93881973,"identity":"9bd1dd21-80f0-4494-a174-47cace86a879","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"eps","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83376,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage11.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/4e6c944bbef2f90d8f073a72.eps"},{"id":93883271,"identity":"06c014e2-457f-49d0-b19e-343c81972203","added_by":"auto","created_at":"2025-10-19 16:59:57","extension":"eps","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":597,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage2.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/3f03186218de06febe3d5fa0.eps"},{"id":93881965,"identity":"9f715b87-75fd-451c-88cf-1851b9c8b548","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"eps","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111926,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage3.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/ca9ef7a90bef1141defb3e16.eps"},{"id":93883516,"identity":"2ece1530-dde0-4fd1-910b-346c94d33416","added_by":"auto","created_at":"2025-10-19 17:07:57","extension":"eps","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":603,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage4.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/34289647231997fd45b9e7ed.eps"},{"id":93881966,"identity":"f24217b2-3f56-410f-b88f-0da3d6d16524","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"eps","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61997,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage5.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/58d3da74821f96be2449f38e.eps"},{"id":93881975,"identity":"3504d37c-a685-4919-b14f-5d052207ed36","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"eps","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":548,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage6.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/04e9437f24acbdc75a5b018c.eps"},{"id":93883272,"identity":"e54caae5-86a5-45b6-8e1d-f2038a5dbd63","added_by":"auto","created_at":"2025-10-19 16:59:57","extension":"eps","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":549,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage7.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/3b30e3dd4873a8f1216b1268.eps"},{"id":93881971,"identity":"92d52a94-543b-47dc-b4b3-1aa24ea02781","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"eps","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":395,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage8.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/312ed778bf760ebc8d5e9940.eps"},{"id":93881977,"identity":"a237c5b1-9913-4cc2-8833-8987bccc06b5","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"eps","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66707,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage9.eps","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/137c0191538ed6820b7e41b2.eps"},{"id":93882002,"identity":"a69a0614-55a7-4898-a4a6-9272628d7fea","added_by":"auto","created_at":"2025-10-19 16:51:59","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53502,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/d70211285b78a2d7bcf54e9b.jpeg"},{"id":93881980,"identity":"213d842f-a57e-4033-a32d-28967a4dfeba","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":67460,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/67b2d95fa6ae3163f4747f49.jpeg"},{"id":93881979,"identity":"c3fac30c-3fdc-42c2-be26-471baadd1b2f","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84330,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/9d2b9ecd6a4437f260c44c86.jpeg"},{"id":93881970,"identity":"919be11e-bfa9-4887-9fad-6d627aa7cbb0","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14496,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/0d76d5d7ee0f609016c00e68.png"},{"id":93881978,"identity":"ed05b11e-ebdc-4b3c-8e6a-28ef07abb93f","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17017,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/691fcd6e4b21174c8f04e11a.png"},{"id":93882001,"identity":"caace4c3-34b5-4142-94ba-864f4d847269","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22346,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/1c70ef3fc0c2c89af73f6de7.png"},{"id":93881968,"identity":"a47ebdcc-df39-41cc-8d25-6adc5e6ea057","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149796,"visible":true,"origin":"","legend":"","description":"","filename":"39fc0530c814408a97325755691cf5d31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/f9e644635186ef4bf46e85ea.xml"},{"id":93881976,"identity":"a010a169-deb7-41bc-9c84-9443dabadde1","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158100,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/83747331d3f9fe5369a7857c.html"},{"id":93883266,"identity":"e7b084be-dc00-49b1-ab58-a87b49b3ae09","added_by":"auto","created_at":"2025-10-19 16:59:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32151,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for the study. NVAF: non-valvular atrial \u0026nbsp;\u0026nbsp;fibrillation; TEE: transesophageal echocardiography; LAT/SEC: left atrial \u0026nbsp;\u0026nbsp;thrombus/spontaneous echo contrast.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/0066ad85abfe660ab90176c0.png"},{"id":93883267,"identity":"444df9fe-17ae-4210-8c6f-5f013b9490e2","added_by":"auto","created_at":"2025-10-19 16:59:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99670,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for the prediction of left atrial thrombus/spontaneous echo contrast (LAT/SEC) of residual cholesterol inflammatory index (RCII) \u003cstrong\u003e[A]\u003c/strong\u003e and left atrial diameter (LAD) \u003cstrong\u003e[B]\u003c/strong\u003e. AUC, area under the curve.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/3d006f57b6a3c79637c2830a.png"},{"id":93881964,"identity":"f61cf161-456c-4270-a90e-7722bcb66c7c","added_by":"auto","created_at":"2025-10-19 16:51:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67506,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating \u0026nbsp;\u0026nbsp;characteristic (ROC) analysis of each prediction model. RCII, residual \u0026nbsp;\u0026nbsp;cholesterol inflammatory index; LAD, left atrial diameter.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/61c21ec96ed5efbc2a172f47.png"},{"id":104880955,"identity":"db79f5a5-9a50-46cf-9c24-9bdc57482dbb","added_by":"auto","created_at":"2026-03-18 09:14:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1427072,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7452942/v1/d7aa6668-ed4e-4180-b9d0-c88b8911ba2f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive value of residual cholesterol inflammatory index for left atrial thrombus or spontaneous echo contrast in patients with nonvalvular atrial fibrillation with low CHA 2 DS 2 -VASc scores","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtrial fibrillation (AF) is the most common persistent arrhythmia in clinical practice, and its incidence is increasing with the improvement of chronic disease survival rate and population aging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Left atrial thrombosis (LAT) is significantly associated with stroke in patients with nonvalvular atrial fibrillation (NVAF). Early assessment of stroke risk and timely anticoagulant therapy are critical to reduce thromboembolic events and mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition, spontaneous echo contrast (SEC) in patients with AF indicates the pre-thrombotic state, which can further thrombosis and is therefore an indication of anticoagulation therapy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt present, the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores are mainly used in clinical practice to assess stroke risk in patients with AF, and anticoagulation therapy is guided according to the score [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Current clinical guidelines recommend anticoagulation therapy for AF patients with high CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores, while there is still debate about whether to give anticoagulant therapy to patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores (women:1\u0026ndash;2 points; men:0\u0026ndash;1 point), who are therefore at risk for LAT/SEC and thromboembolism [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eResidual cholesterol (RC) is a triglyceride-rich lipoprotein cholesterol composed of very low-density lipoprotein (VLDL), medium-density lipoprotein (IDL), and chylomicron residues [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is not only closely related to the occurrence and development of atherosclerosis but also a risk factor for hypertension, aortic stenosis, stroke, and death from cardiovascular disease [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. RC can be deposited in the lining of blood vessels, leading to endothelial dysfunction and vascular inflammation. In addition, the triglycerides in RC can be broken down into free fatty acids and monoacylglycerol, thus aggravating the body's inflammatory response [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies have shown that elevated levels of RC and highly sensitive C-reactive protein (hs-CRP) can reflect low-grade inflammation in the body [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Elevated RC levels, combined with persistent low-grade inflammation, may promote the development of LAT/SEC in patients with NVAF and further influence stroke development.\u003c/p\u003e\u003cp\u003eResidual cholesterol inflammatory index (RCII), calculated by RC and hs-CRP, provides a comprehensive assessment of residual cholesterol and low-grade inflammation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, the purpose of this study was to evaluate the predictive ability of RCII for LAT/SEC in patients with NVAF with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score, to identify patients whose CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores failed to detect thrombosis, give timely anticoagulant therapy, and improve their prognosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a single-center retrospective cohort study. This study collected data from 2681 patients with NVAF through the inpatient electronic medical record system, all of whom were admitted to the cardiology Department of the Second Hospital of Hebei Medical University between January 2022 and December 2023 and underwent transesophageal echocardiography (TEE) and transthoracic echocardiography (TTE).\u003c/p\u003e\n\u003cp\u003eInclusion criteria: 1) age\u0026gt;18 years old; 2) transthoracic and transesophageal echocardiography were completed, and the relevant clinical data were complete; 3) non-valvular atrial fibrillation. Exclusion criteria: 1) patients with high CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores (women\u0026ge;3; men\u0026ge;2); 2) patients with heart valve disease or who have previously undergone valve replacement or remodeling surgery; 3) patients with congenital heart disease; 4) Patients with cardiomyopathy; 5) patients with autoimmune diseases, hyperthyroidism, and other systemic diseases; 6) patients with severe hepatic and renal insufficiency or malignant tumor; 7) complicated with acute myocardial infarction or acute heart failure; 8) patients with incomplete clinical data. Eventually, 967 patients were enrolled in the study. All relevant information, including general clinical data, echocardiogram results, and laboratory test results, was collected from the electronic medical record system. \u003cstrong\u003eFig 1\u0026nbsp;\u003c/strong\u003eshows the flow chart of the study. Ethical approval was obtained from the Ethics Committee of The Second Hospital of Hebei Medical University. The research was conducted according to the Helsinki Declaration guidelines.\u003c/p\u003e\n\u003cp\u003eThe diagnosis of AF is based on the ECG characteristics of AF on the routine 12 lead electrocardiograms, the persistent event of atrial fibrillation \u0026gt; 30s on the 24-hour Holter electrocardiogram, or the presence of a previous episode of AF. Hypertension is defined as systolic blood pressure \u0026ge;140 mmHg and/or diastolic blood pressure \u0026ge;90 mmHg, or the use of antihypertensive drugs. To diagnose diabetes, fasting serum glucose levels of at least 7.0 mmol/L and/or random glucose levels of at least 11.1 mmol/L were required. Congestive heart failure is diagnosed based on characteristic symptoms and subsequently confirmed by a physician\u0026rsquo;s diagnosis. Peripheral artery disease (PAD) was diagnosed using vascular Doppler ultrasound or past medical history. The diagnosis of ischemic stroke is based on imaging evidence or ischemic stroke history. The diagnosis of coronary heart disease is based on relevant clinical guidelines or coronary heart disease history. Transthoracic echocardiography and transesophageal echocardiography were examined and measured by experienced senior sonographers, in which the left ventricular ejection fraction (LVEF) was measured by the modified Simpson method, and the left atrial diameter (LAD) was measured by the anterior and posterior diameters of the left atrium [17].\u003csup\u003e\u0026nbsp;\u003c/sup\u003eRC (mg/dL) was calculated as: RC=TC-(HDL-C+LDL-C). RCII was calculated by multiplying RC by hs-CRP, RCII=RC(mg/dL)\u0026times;hs-CRP(mg/L)/10 [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCHA2DS2-VASc scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the collected clinical information, the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores were recalculated. 1 point is assigned for each risk variable, including congestive heart failure or left ventricular dysfunction, hypertension, diabetes, and vascular disease. Patients aged 65-74 years scored 1 point, and patients aged \u0026ge;75 years scored 2 points. Females get an extra point. 2 for stroke or transient ischemic attack. Low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores included scores of 1-2 in female patients and 0-1 in male patients [18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEchocardiographic examination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients underwent transesophageal echocardiography to determine the presence of LAT or SEC. The diagnostic criteria for LAT are mobile, independent, round, or irregular in shape, uniform in density but different from that of the surrounding myocardial tissue, and can be detected in multiple parts of the left atrial lumen [19]. The diagnostic criteria for SEC are smoke, swirl, or pre-thrombotic states in the left atrium, but are distinct from the illusion caused by high-gain and near-field artifact changes [20]. Data related to cardiac cavity size and ventricular wall motion were collected by completing TTE. The echocardiogram is performed by two professional ultrasound physicians, one of whom is responsible for completing the procedure and making the diagnosis, while the other is responsible for reviewing the results. Neither doctor was aware of the patient\u0026apos;s clinical condition before the examination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using SPSS (Version 26.0, SPSS Inc., Chicago, IL, USA). Categorical variables were compared between groups using the \u0026chi;\u003csup\u003e2\u003c/sup\u003e test and expressed as numbers (%). Continuous variables were first tested for normality by Kolmogorov-Smirnov, continuous variables with normal distribution were analyzed by t-test and expressed as mean \u0026plusmn; standard deviation and continuous variables with non-normal distribution were analyzed by Mann-Whitney U test. Logistic regression analysis was used to explore the influencing factors of LAT/SEC formation in patients with NVAF. The predictive power of RCII and other risk variables was analyzed by mapping receiver operating characteristics (ROC). With two-sided \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, the difference was considered to be statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e\n \u003cp\u003eA total of 967 patients with NVAF were enrolled, with a mean age of 55.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.81 years. According to the results of transesophageal echocardiography, they were divided into the non-LAT /SEC group (n\u0026thinsp;=\u0026thinsp;849) and the LAT/SEC group (n\u0026thinsp;=\u0026thinsp;118). In the LAT/SEC group, 75 patients developed LAT, of which 25 patients combined with SEC and only 43 patients developed SEC. The incidence of LAT and SEC accounted for 7.76% and 4.45% of the population, respectively. As shown in \u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e, heart failure (HF) (16.95% vs. 2.24%) was more prevalent in the LAT/SEC group, with higher body mass index (BMI) levels [27.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77 kg/m\u003csup\u003e2\u003c/sup\u003e vs. 26.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67 kg/m\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01], total cholesterol (TC) levels[181.72 (156.70-208.29) mg/dL vs. 166.32 (145.15-190.19) mg/dL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01], and lipoprotein(a) (Lp(a)) levels [15.63 (7.09\u0026ndash;26.13) mg/L vs. 11.91 (6.11\u0026ndash;23.42) mg/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023]compared to the non-LAT /SEC group. In addition, serum uric acid (SUA) [367.10\u0026thinsp;\u0026plusmn;\u0026thinsp;90.81 umol/L vs. 340.83\u0026thinsp;\u0026plusmn;\u0026thinsp;94.38 umol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005], aspartate aminotransferase (AST) [20.00 (16.30\u0026ndash;26.00) U/L vs. 18.65 (15.20\u0026ndash;23.00) U/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006], high-sensitivity C-reactive protein (hs-CRP) [2.00 (1.48\u0026ndash;4.48) mg/L vs. 1.70 (1.00-3.98) mg/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01], monocyte count [0.60 (0.43\u0026ndash;0.80)\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L vs. 0.54 (0.41\u0026ndash;0.70)\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021], red blood cell count (RBC) [4.67 (4.38\u0026ndash;5.03) g/L vs. 4.54 (4.23\u0026ndash;4.89) g/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004], red cell distribution width standard deviation (RDWSD) [43.80 (42.00-45.03) fl vs. 42.90 (41.10\u0026ndash;44.60) fl, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017], and residual cholesterol inflammatory index (RCII) [9.04 (5.18\u0026ndash;16.90) vs. 3.81 (2.00-7.84), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01] were also higher in this group. Left ventricular ejection fraction (LVEF) [60.00 (51.75\u0026ndash;61.70) % vs. 61.40 (59.10\u0026ndash;65.40) %, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01], Lymphocyte count [1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L vs. 1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003] and paroxysmal atrial fibrillation (AF) (55.90% vs. 88%) in LAT/SEC group were lower. No statistical differences were observed in terms of gender, age, systolic blood pressure, diastolic blood pressure, CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores, fasting plasma glucose (FPG), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum creatinine (SCr), alanine aminotransferase (ALT), white blood cell count (WBC), neutrophil count, platelet count (PLT), platelet distribution width (PDW), mean platelet volume (MPV), smoke, alcohol consumption, previous diabetes, ischemic stroke, peripheral vascular disease (PAD), hyperlipidemia, chronic kidney disease (CKD), and pre-hospitalization medication.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eTable.1. Baseline characteristics of NVAF patients with/without LAT/SEC.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n\u0026thinsp;=\u0026thinsp;967)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-LAT/SEC (n\u0026thinsp;=\u0026thinsp;849)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAT/SEC (n\u0026thinsp;=\u0026thinsp;118)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e651(67.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e562(66.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(75.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years old)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.00 (51.00\u0026ndash;62.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.00 (51.00\u0026ndash;62.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.50 (53.00\u0026ndash;62.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126(117\u0026ndash;135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126(117\u0026ndash;135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125(113\u0026ndash;133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.00 (75.00\u0026ndash;89.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.00 (75.00\u0026ndash;89.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.00 (74.00\u0026ndash;91.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.00 (34.00\u0026ndash;39.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.00 (33.00\u0026ndash;39.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.50 (38.00\u0026ndash;41.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEDD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.00 (45.00\u0026ndash;50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.00 (45.00\u0026ndash;49.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.00 (46.00-52.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.00 (58.50\u0026ndash;65.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.40 (59.10\u0026ndash;65.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.00 (51.75\u0026ndash;61.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE/e\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.29 (8.14\u0026ndash;11.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.19 (8.10-11.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.45 (8.86\u0026ndash;12.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.98 (4.54\u0026ndash;5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.99 (4.53\u0026ndash;5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.92 (4.55\u0026ndash;5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.98(80.63-159.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.75 (80.63-160.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.89 (78.85-147.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.09 (146.69-192.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166.32 (145.15-190.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181.72 (156.70-208.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.57 (35.22\u0026ndash;48.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.57 (35.60-48.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.41 (34.44\u0026ndash;48.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.01\u0026thinsp;\u0026plusmn;\u0026thinsp;30.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.39\u0026thinsp;\u0026plusmn;\u0026thinsp;30.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.52\u0026thinsp;\u0026plusmn;\u0026thinsp;30.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLp(a) (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.48 (6.31\u0026ndash;24.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.91 (6.11\u0026ndash;23.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.63 (7.09\u0026ndash;26.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSCr (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.00 (61.00\u0026ndash;82.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.00 (61.00\u0026ndash;82.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.00 (61.00\u0026ndash;82.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUA (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344.03\u0026thinsp;\u0026plusmn;\u0026thinsp;94.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e340.83\u0026thinsp;\u0026plusmn;\u0026thinsp;94.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367.10\u0026thinsp;\u0026plusmn;\u0026thinsp;90.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.70 (14.25\u0026ndash;28.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.25 (14.00-28.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.40 (16.15\u0026ndash;29.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.00 (15.30\u0026ndash;23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.65 (15.20\u0026ndash;23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.00 (16.30\u0026ndash;26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehs-CRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80 (1.00\u0026ndash;4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70 (1.00-3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00 (1.48\u0026ndash;4.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.90 (4.90\u0026ndash;6.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.90 (4.90\u0026ndash;6.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.72 (4.91\u0026ndash;7.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.38 (2.67\u0026ndash;4.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.37 (2.63\u0026ndash;4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.40 (2.72\u0026ndash;4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55 (0.42\u0026ndash;0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54 (0.41\u0026ndash;0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60 (0.43\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.57 (4.25\u0026ndash;4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.54 (4.23\u0026ndash;4.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67 (4.38\u0026ndash;5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDWSD (fl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.90 (41.30\u0026ndash;44.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.90 (41.10\u0026ndash;44.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.80 (42.00-45.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208.00 (175.00-244.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209.40 (177.00-246.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201.00 (169-233.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDW (fl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.40 (13.10\u0026ndash;16.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.40 (13.10\u0026ndash;16.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.40 (13.65-17.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPV (fl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.20 (8.30\u0026ndash;10.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.20 (8.30\u0026ndash;10.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.33 (8.50\u0026ndash;10.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRCII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58 (2.23\u0026ndash;8.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81 (2.00-7.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.04 (5.18\u0026ndash;16.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParoxysmal AF, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e813(84.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e747 (88.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (55.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181 (18.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153 (18.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (23.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161 (16.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 (15.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (22.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHF, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (16.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327 (33.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e291 (34.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (30.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (3.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIschemic stroke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (4.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (14.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCKD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003ePrehospital medication, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWarfarin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNOAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151(15.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 (15.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(14.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntiplatelet agent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 (12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104 (12.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (10.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151 (15.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; LAD, left atrium diameter; LVEDD, left ventricular end-diastolic dimension; LVEF, left ventricular ejection fraction; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SCr, serum creatinine; SUA, serum uric acid; ALT, alanine aminotransferase; AST, aspartate aminotransferase; hs-CRP, high-sensitivity C-reactive protein; WBC, white blood cell count; RBC, red blood cell count; RDWSD, red cell distribution width standard deviation; PLT, platelet count; PDW, platelet distribution width; MPV, mean platelet volume; RCII, residual cholesterol inflammatory index; HF, heart failure; PAD, peripheral vascular disease; AF, atrial fibrillation; CKD, chronic kidney disease; NOAC, new oral anticoagulants; Lp(a), lipoprotein(a).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eRisk factors for LAT/SEC\u003c/h3\u003e\n\u003cp\u003eVariables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the baseline table were selected to be included in univariable logistics regression to determine the risk factors for the formation of LAT/SEC and multivariate logistics regression analysis was performed. The results showed that LAD, lipoprotein(a), hs-CRP, lymphocyte count, monocyte count, history of heart failure, non-paroxysmal atrial fibrillation, and RCII were all predictors of LAT/SEC (\u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eSubjects were divided into four groups according to the quartile of RCII and LAD levels. Multivariate logistic regression analysis showed that RCII level was correlated with LAT/SEC (\u003cstrong\u003eTable\u0026nbsp;3\u003c/strong\u003e). Specifically, patients with the highest quartile array showed a higher incidence of LAT/SEC compared to the lowest quartile array. After adjusting for age and gender in Model I and other confounding factors including gender, age, body mass index, left ventricular ejection fraction, left ventricular end-diastolic dimension, E/e\u0026apos;, total cholesterol, lipoprotein(a), serum uric acid, alanine aminotransferase, aspartate aminotransferase, high-sensitivity C-reactive protein, heart failure, and paroxysmal atrial fibrillation in Model II, the association between RCII and LAT/SEC formation remained consistent.\u003c/p\u003e\n\u003cp\u003eRegardless of adjusting for confounding factors, the incidence of LAT/SEC increased significantly with the enlargement of the left atrium. As shown in \u003cstrong\u003eFig.\u0026nbsp;2\u003c/strong\u003e, the ROC curve was drawn to analyze the prediction efficiency of RCII and LAD for LAT/SEC. The results showed that the area under the curve (AUC) of RCII was 0.746 (95% CI 0.705\u0026ndash;0.787, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while that of LAD was 0.736 (95% CI 0.707\u0026ndash;0.764, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eTable.2. Univariable and multivariate logistic regression for risk factors of LAT/SEC.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.018\u0026ndash;1.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000-1.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.130\u0026ndash;1.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.034\u0026ndash;1.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.050\u0026ndash;1.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.952\u0026ndash;1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.908\u0026ndash;0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.962\u0026ndash;1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE/e\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.027\u0026ndash;1.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.922\u0026ndash;1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.005\u0026ndash;1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992\u0026ndash;1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLp(a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004\u0026ndash;1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001\u0026ndash;1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001\u0026ndash;1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u0026ndash;1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u0026ndash;1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehs-CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.017\u0026ndash;1.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.457\u0026ndash;0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.422\u0026ndash;0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.375\u0026ndash;0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocyte count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.075\u0026ndash;7.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.753\u0026ndash;10.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.230\u0026ndash;2.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.905\u0026ndash;2.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDWSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.012\u0026ndash;1.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.977\u0026ndash;1.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRCII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.033\u0026ndash;1.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.159\u0026ndash;1.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParoxysmal AF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.114\u0026ndash;0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u0026ndash;0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.599\u0026ndash;17.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.240\u0026ndash;8.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. BMI, body mass index; LAD, left atrium diameter; LVEDD, left ventricular end-diastolic dimension; LVEF, left ventricular ejection fraction; TC, total cholesterol; SUA, serum uric acid; ALT, alanine aminotransferase; AST, aspartate aminotransferase; hs-CRP, high-sensitivity C-reactive protein; RBC, red blood cell count; RDWSD, red cell distribution width standard deviation; RCII, residual cholesterol inflammatory index; HF, heart failure; AF, atrial fibrillation; Lp(a), lipoprotein(a); LAT/SEC left atrial thrombosis/spontaneous echo contrast.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eTable.3. Univariable and multivariate logistic regression for risk factors of LAT/SEC.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRCII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (1.03\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.04 (1.03\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (1.02\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.96 (1.80-35.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.53 (1.46\u0026ndash;29.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.98 (1.55\u0026ndash;41.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.88 (5.70-100.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20.82 (4.92\u0026ndash;88.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.21 (4.61-116.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.22 (9.71-166.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31.86 (7.62-133.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.93 (3.47-103.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (1.13\u0026ndash;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.17 (1.12\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (1.04\u0026ndash;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.89 (2.28\u0026ndash;20.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6.85 (2.24\u0026ndash;20.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.11 (1.55\u0026ndash;41.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.47 (4.73\u0026ndash;38.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11.58 (4.01\u0026ndash;33.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.78 (3.37\u0026ndash;41.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.19 (8.99\u0026ndash;70.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20.75 (7.31\u0026ndash;58.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.71 (3.52\u0026ndash;45.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. RCII, residual cholesterol inflammatory index; LAD left atrial diameter; LAT/SEC, left atrial thrombosis/spontaneous echo contrast; OR, odds ratio; CI, confidence interval. Model I adjusted for gender and age. Mode II adjusted for gender, age, body mass index, left ventricular ejection fraction, left ventricular end-diastolic dimension, E/e\u0026apos;, total cholesterol, lipoprotein(a), serum uric acid, alanine aminotransferase, aspartate aminotransferase, high-sensitivity C-reactive protein, heart failure, and paroxysmal atrial fibrillation. For the RCII group: Q1: RCII\u0026thinsp;\u0026le;\u0026thinsp;2.23; Q2: 2.23\u0026thinsp;\u0026lt;\u0026thinsp;RCII\u0026thinsp;\u0026le;\u0026thinsp;4.58; Q3: 4.58\u0026thinsp;\u0026lt;\u0026thinsp;RCII\u0026thinsp;\u0026le;\u0026thinsp;8.81; Q4: RCII\u0026thinsp;\u0026gt;\u0026thinsp;8.81. For the LAD group: Q1: LAD\u0026thinsp;\u0026le;\u0026thinsp;34 mm; Q2: 34\u0026thinsp;\u0026lt;\u0026thinsp;LAD\u0026thinsp;\u0026le;\u0026thinsp;37mm; Q3: 37\u0026thinsp;\u0026lt;\u0026thinsp;LAD\u0026thinsp;\u0026le;\u0026thinsp;39 mm; Q4: LAD\u0026thinsp;\u0026gt;\u0026thinsp;39 mm.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eRCII and LAD enhance the prediction accuracy of the CHADS‑VASc score\u003c/h3\u003e\n\u003cp\u003eIncreased RCII and left atrial enlargement are risk factors for LAT/SEC formation. RCII and LAD were incorporated into the new forecast model. As shown in \u003cstrong\u003eTable\u0026nbsp;4\u003c/strong\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores had poor predictive power for LAT/SEC (AUC\u0026thinsp;=\u0026thinsp;0.502, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). After the inclusion of RCII (Model A), the prediction efficiency was significantly improved (AUC difference\u0026thinsp;=\u0026thinsp;0.248, \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.350, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). After combining RCII and LAD (Model B), the model prediction efficiency was improved (AUC difference\u0026thinsp;=\u0026thinsp;0.285, \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.589, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The prediction efficiency of Model B was higher than that of Model A, and the difference was statistically significant (AUC difference\u0026thinsp;=\u0026thinsp;0.037, \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.061, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tabe\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eTable.4. Comparison between the prediction models.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026Dagger;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.470\u0026ndash;0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score\u0026thinsp;+\u0026thinsp;RCII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.709\u0026ndash;0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.350\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score\u0026thinsp;+\u0026thinsp;RCII\u0026thinsp;+\u0026thinsp;LAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.748\u0026ndash;0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.589\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.061\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cstrong\u003e\u0026dagger;\u003c/strong\u003e\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e-value for each ROC curve analysis\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026Dagger;\u003c/sup\u003eThe \u003cem\u003eP\u003c/em\u003e-value when comparing between the two models\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003eCompared to the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score group\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e**\u003c/sup\u003eCompared to the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score\u0026thinsp;+\u0026thinsp;RCII group\u003c/p\u003e\n \u003cp\u003eAUC, area under the receiver operating characteristic curve, CI, confidence interval, LAD, left atrial diameter, RCII, residual cholesterol inflammatory index.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAtrial fibrillation (AF) is the most common arrhythmia, and because there are usually no obvious symptoms, it significantly increases the risk of stroke and all-cause death [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Studies have shown that SEC and LAT are significantly correlated with AF-related embolism [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Early identification of high-risk patients and timely anticoagulation therapy are of great significance for improving patient prognosis. Current clinical guidelines recommend anticoagulation therapy for patients with high CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores, while there is still controversy over whether patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores (0\u0026ndash;1 in men and 1\u0026ndash;2 in women) should be treated with anticoagulation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Many studies have shown that the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores have limited predictive power in clinical practice, especially for patients with low thrombosis scores and poor sensitivity to LAT [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Previous studies have shown that patients with NVAF with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores will still have thromboembolic events, among which the annual stroke rate of women with a score of 2 and men with a score of 1 is about 3%, while the incidence of thrombotic events is as high as 11.4% in patients with a score of 0\u0026ndash;1 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores may be delayed to some extent for low-risk patients without previous embolic events only after the occurrence of embolic events, and this scoring system mainly takes stroke as the main outcome event rather than left atrial thrombosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn patients with NVAF, LAT may occur when atrial fibrillation lasts longer than 48 hours, and SEC is an important marker of thrombosis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. LAT and SEC were independently associated with thromboembolic events in patients with atrial fibrillation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Previous studies have shown that the average incidence of LAT in patients with NVAF is about 9.8% [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Another study showed that the incidence of LAT and SEC in 481 NVAF patients was 12.47% and 11.43%, respectively [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the incidence rates of SEC and LAT in this study were 4.45% and 7.76%, respectively, lower than those reported in previous studies. This difference between the results of the studies may be related to demographic differences in the included populations and potential selection bias in the studies. Therefore, this study was designed to evaluate patients at high risk of thromboembolism with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores, to provide references for clinical treatment to improve patient outcomes.\u003c/p\u003e\u003cp\u003eResidual cholesterol (RC) refers to the cholesterol content in all triglyceride-rich lipoprotein (TRL), including the sum of the cholesterol in fasting very low-density lipoprotein (VLDL) and intermediate density lipoprotein (IDL) and the cholesterol in non-fasting chylomicron [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It is calculated by subtracting high-density lipoprotein cholesterol (HDL-C) and high-density lipoprotein cholesterol (LDL-C) from total cholesterol (TC) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Recent studies have found that residual cholesterol (RC) is associated with an increased risk of cardiovascular diseases such as myocardial infarction, atrial fibrillation, and ischemic stroke [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Results from a genetic study show that RC has a greater effect on cardiovascular risk than LDL-C [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Compared with LDL-C, RC can carry more cholesterol and is more easily captured by macrophages, so RC has a stronger ability to cause arteriosclerosis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition, RC can promote the expression of cytokines interleukin and other inflammatory mediators, leading to chronic low-grade inflammation, and then promote the formation and progression of atherosclerosis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Another study showed that in the LDL-C quartile group, the highest group had a significantly lower cumulative incidence of atrial fibrillation than the lowest group [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, there is currently a cholesterol paradox and there is still little evidence for a relationship between RC and thromboembolic risk in AF, so this study aimed to evaluate the relationship between RC and LAT/SEC in NVAF.\u003c/p\u003e\u003cp\u003eAtrial remodeling caused by inflammatory states is the basis for the occurrence and maintenance of atrial fibrillation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. C-reactive protein (CRP), as a commonly used clinical indicator of inflammation, is associated with the occurrence of AF [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Previous studies have shown that CRP is slightly elevated in patients with AF, which may reflect the chronic inflammatory state of the body to a certain extent, which can lead to the remodeling of the heart and blood vessels, and thus promote the occurrence of AF [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In addition, previous studies have shown that elevated CRP increases the risk of atrial myocyte calcium influx [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A retrospective study showed that IL-6, highly sensitive C-reactive protein (hs-CRP), and white blood cell count were positively correlated with the occurrence of AF in elderly patients [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. CRP as an inflammatory marker, can predict the recurrence of AF after electrocardioversion [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Residual Cholesterol Inflammation Index (RCII), calculated by RC and hs-CRP, provides a comprehensive assessment of residual cholesterol and low-grade inflammation. Therefore, this study aimed to evaluate the predictive power of RCII for LAT/SEC in NVAF patients with low CHA2DS2-VASc scores. The study structure shows that RCII has a good predictive ability for LAT/SEC, the area under the curve is 0.746, and the optimal critical value is 4.46. In this study, in addition to RCII, left atrial diameter (LAD) was also found to be a predictor of LAT/SEC. Changes in atrial structure, such as atrial cardiomyocyte hypertrophy and fibrosis, and atrial dilation, can lead to shortened action potential, reduced electrical connections between cells, and altered Ca\u003csup\u003e2+\u003c/sup\u003e processing [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study showed a significant increase in LAD in the LAT/SEC group compared to the non-LAT /SEC group (39.50 (38.00\u0026ndash;41.00) vs. 37.00 (33.00\u0026ndash;39.00), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After combining RCII and LAD, the ability of the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores to predict LAT/SEC was significantly improved (AUC difference\u0026thinsp;=\u0026thinsp;285, \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.589, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, in assessing the risk of thrombosis in NVAF patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores, when RCII\u0026thinsp;\u0026ge;\u0026thinsp;4.46 and LAD\u0026thinsp;\u0026ge;\u0026thinsp;36.5 mm indicate an increased likelihood of LAT formation, TEE should be actively performed to detect intra-atrial thrombosis.\u003c/p\u003e\u003cp\u003eAt present, there are few studies on the incidence of LAT/SEC in NVAF patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores. This study was the first to establish the predictive efficacy of RCII for the occurrence of LAT/SEC in patients with NVAF, with an optimal cut-off value of 4.46. In addition, left atrial enlargement was found to be an independent risk factor for LAT/SEC, which was consistent with previous studies. This study found that the combination of RCII, LAD, and CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores could significantly enhance the prediction efficiency of LAT/SEC. Therefore, RCII and LAD should be combined to assess thromboembolic risk in NVAF patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores. This helps to assess and identify patients with potential thrombosis as early as possible, perform TEE examination promptly, and administer anticoagulant therapy to improve patient outcomes.\u003c/p\u003e\u003cp\u003eHowever, there are some limitations in this study. First of all, this study is a single-center retrospective study, which may be biased. In addition, the relevant clinical data collected in this study were limited, only LAD was used to evaluate left atrial structure, and no other indicators of left atrial function were collected. Therefore, whether RCII can be used in the clinical assessment of patients' thrombosis risk remains to be further confirmed by large-scale prospective studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, increased RCII and LAD were independent risk factors for LAT/SEC in NVAF patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores. CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores combined with RCII and LAD can significantly improve its prediction efficiency. Therefore, RCII, LAD, and CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores should be combined for comprehensive analysis in assessing thromboembolism risk in patients with NVAF.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with the Declaration of Helsinki. The Ethics Review Committee of the second hospital of Hebei medical university approved the study and written informed consents were obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKornej J, B\u0026ouml;rschel CS, Benjamin EJ, et al. Epidemiology of Atrial Fibrillation in the 21st Century: Novel Methods and New Insights. Circ Res. 2020;127(1):4-20.\u003c/li\u003e\n\u003cli\u003eJame S, Barnes G. Stroke and thromboembolism prevention in atrial fibrillation. Heart. 2020;106(1):10-17.\u003c/li\u003e\n\u003cli\u003eShi B, Suo R, Song W, et al. Plasma metabolomic characteristics of atrial fibrillation patients with spontaneous echo contrast. BMC Cardiovasc Disord. 2024;24(1):654.\u003c/li\u003e\n\u003cli\u003eJoglar JA, Chung MK, Armbruster AL, et al. 2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2024;149(1):e167.\u003c/li\u003e\n\u003cli\u003eHao L, Chen X, Sun W, et al. Risk Factors for Silent Brain Infarction in Nonvalvular Atrial Fibrillation Patients with Low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc Score. Curr Neurovasc Res. 2024.\u003c/li\u003e\n\u003cli\u003eKim YK, Lee SR, Choi EK, et al. Risk of Death From Various Causes According to Prevalent Atrial Fibrillation: A Nationwide Population-Based Study. J Korean Med Sci. 2024;39(47):e306.\u003c/li\u003e\n\u003cli\u003eOuyang X, Tang X, Peng L, et al. Remnant cholesterol and new-onset atrial fibrillation: The Atherosclerosis Risk in Communities study. Heart Rhythm. 2024;S1547-5271(24)03456-3458.\u003c/li\u003e\n\u003cli\u003eZhang K, Qi X, Zhu F, et al. Remnant cholesterol is associated with cardiovascular mortality. Front Cardiovasc Med. 2022;9:984711. \u003c/li\u003e\n\u003cli\u003eNavarese EP, Vine D, Proctor S, et al. Independent Causal Effect of Remnant Cholesterol on Atherosclerotic Cardiovascular Outcomes: A Mendelian Randomization Study. Arterioscler Thromb Vasc Biol. 2023;43(9):e373-e380.\u003c/li\u003e\n\u003cli\u003eLi ZH, Hao QY, Zeng YH, et al. Remnant cholesterol and the risk of aortic valve calcium progression: insights from the MESA study. Cardiovasc Diabetol. 2024;23(1):20.\u003c/li\u003e\n\u003cli\u003eGuo DC, Gao JW, Wang X, et al. Remnant cholesterol and risk of incident hypertension: a population-based prospective cohort study. Hypertens Res. 2024;47(5):1157-1166.\u003c/li\u003e\n\u003cli\u003eVan Diepen JA, Berb\u0026eacute;e JF, Havekes LM, et al. Interactions between inflammation and lipid metabolism: relevance for efficacy of anti-inflammatory drugs in the treatment of atherosclerosis. Atherosclerosis. 2013;228(2):306-315.\u003c/li\u003e\n\u003cli\u003eChait A, Ginsberg HN, Vaisar T, et al. Remnants of the Triglyceride-Rich Lipoproteins, Diabetes, and Cardiovascular Disease. Diabetes. 2020;69(4):508-516.\u003c/li\u003e\n\u003cli\u003eWadstr\u0026ouml;m BN, Pedersen KM, Wulff AB, et al. Elevated remnant cholesterol, plasma triglycerides, and cardiovascular and non-cardiovascular mortality. Eur Heart J. 2023;44(16):1432-1445.\u003c/li\u003e\n\u003cli\u003eSmall AM, Pournamdari A, Melloni GEM, et al. Lipoprotein(a), C-Reactive Protein, and Cardiovascular Risk in Primary and Secondary Prevention Populations. JAMA Cardiol. 2024;9(4):385-391.\u003c/li\u003e\n\u003cli\u003eChen J, Wu Q, Liu H, et al. Predictive value of remnant cholesterol inflammatory index for stroke risk: Evidence from the China health and Retirement Longitudinal study. J Adv Res. 2024. 2024;S2090-1232(24)00592-597.\u003c/li\u003e\n\u003cli\u003eLang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging. 2015;16(3):233-270.\u003c/li\u003e\n\u003cli\u003eHindricks G, Potpara T, Dagres N, et al. Corrigendum to: 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J. 2021;42(40):4194.\u003c/li\u003e\n\u003cli\u003eKoca V, Bozat T, Akkaya V, et al. Left atrial thrombus detection with multiplane transesophageal echocardiography: an echocardiographic study with surgical verification. J Heart Valve Dis. 1999;8(1):63-66.\u003c/li\u003e\n\u003cli\u003eBlack IW, Hopkins AP, Lee LC, et al. Left atrial spontaneous echo contrast: a clinical and echocardiographic analysis. J Am Coll Cardiol. 1991;18(2):398-404.\u003c/li\u003e\n\u003cli\u003eYildirim M, Milles BR, Hund H, et al. Outcomes and Disease Management in Patients With Atrial Fibrillation \u0026ge;80\u0026thinsp;Years: Data From a Consecutive 11-Year Real-World Registry. J Am Heart Assoc. 2025;14(4):e036832.\u003c/li\u003e\n\u003cli\u003eLi Z, Pan L, Deng Y, et al. Development and Validation of a Nomogram for Estimation of Left Atrial Thrombus or Spontaneous Echo Contrast Risk in Non-Valvular Atrial Fibrillation Patients with Low to Borderline CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc Score. Int J Gen Med. 2022;15:7329-7339.\u003c/li\u003e\n\u003cli\u003eAngebrandt Belo\u0026scaron;ević P, \u0026Scaron;malcelj A, Kos N, et al. Left Ventricular Ejection Fraction Can Predict Atrial Thrombosis Even in Non-High-Risk Individuals with Atrial Fibrillation. J Clin Med. 2022;11(14):3965.\u003c/li\u003e\n\u003cli\u003eChen J, Zhou M, Wang H, et al. Risk factors for left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score. J Thromb Thrombolysis. 2022;53(2):523-531.\u003c/li\u003e\n\u003cli\u003eYan S, Li Q, Xia Z, et al. Risk factors of thromboembolism in nonvalvular atrial fibrillation patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score. Medicine (Baltimore). 2019;98(8):e14549.\u003c/li\u003e\n\u003cli\u003eXie X, Li T. Anticoagulation for atrial fibrillation patients with the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score =1 (beyond sex). Acta Cardiol. 2021;76(3):258-264.\u003c/li\u003e\n\u003cli\u003eKamili K, Zheng T, Luo C, et al. Predictive value of lipoprotein(a) for left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores: a cross-sectional study. Lipids Health Dis. 2024;23(1):22.\u003c/li\u003e\n\u003cli\u003eKim YG, Shim J, Oh SK, et al. Risk Factors for Ischemic Stroke in Atrial Fibrillation Patients Undergoing Radiofrequency Catheter Ablation. Sci Rep. 2019;9(1):7051.\u003c/li\u003e\n\u003cli\u003eYoo J, Song D, Baek JH, et al. Poor Outcome of Stroke Patients With Atrial Fibrillation in the Presence of Coexisting Spontaneous Echo Contrast. Stroke. 2016;47(7):1920-1922.\u003c/li\u003e\n\u003cli\u003eDi Minno MN, Ambrosino P, Dello Russo A, et al. Prevalence of left atrial thrombus in patients with non-valvular atrial fibrillation. A systematic review and meta-analysis of the literature. Thromb Haemost. 2016;115(3):663-677.\u003c/li\u003e\n\u003cli\u003eMakover ME, Surma S, Banach M, et al. Eliminating atherosclerotic cardiovascular disease residual risk. Eur Heart J. 2023;44(45):4731-4733.\u003c/li\u003e\n\u003cli\u003eMach F, Baigent C, Catapano AL, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41(1):111-188.\u003c/li\u003e\n\u003cli\u003eLi W, Huang Z, Fang W, et al. Remnant Cholesterol Variability and Incident Ischemic Stroke in the General Population. Stroke. 2022;53(6):1934-1941.\u003c/li\u003e\n\u003cli\u003eJ\u0026oslash;rgensen AB, Frikke-Schmidt R, West AS, et al. Genetically elevated non-fasting triglycerides and calculated remnant cholesterol as causal risk factors for myocardial infarction. Eur Heart J. 2013;34(24):1826-1833.\u003c/li\u003e\n\u003cli\u003eZhong L, Xie B, Wang HL, et al. Causal association between remnant cholesterol level and risk of cardiovascular diseases: a bidirectional two sample mendelian randomization study. Sci Rep. 2024;14(1):27038.\u003c/li\u003e\n\u003cli\u003eBj\u0026ouml;rnson E, Adiels M, Taskinen MR, et al. Triglyceride-rich lipoprotein remnants, low-density lipoproteins, and risk of coronary heart disease: a UK Biobank study. Eur Heart J. 2023;44(39):4186-4195.\u003c/li\u003e\n\u003cli\u003eCasta\u0026ntilde;er O, Pint\u0026oacute; X, Subirana I, et al. Remnant Cholesterol, Not LDL Cholesterol, Is Associated With Incident Cardiovascular Disease. J Am Coll Cardiol. 2020;76(23):2712-2724.\u003c/li\u003e\n\u003cli\u003eBaratta F, Cocomello N, Coronati M, et al. Cholesterol Remnants, Triglyceride-Rich Lipoproteins and Cardiovascular Risk. Int J Mol Sci. 2023;24(5):4268.\u003c/li\u003e\n\u003cli\u003eLi L, Wang C, Ye Z, et al. Association Between Remnant Cholesterol and Risk of Incident Atrial Fibrillation: Population-Based Evidence From a Large-Scale Prospective Cohort Study. J Am Heart Assoc. 2024;13(10):e033840.\u003c/li\u003e\n\u003cli\u003eGalv\u0026atilde;o Braga C, Ramos V, Vieira C, et al. New-onset atrial fibrillation during acute coronary syndromes: predictors and prognosis. Rev Port Cardiol. 2014;33(5):281-287.\u003c/li\u003e\n\u003cli\u003eSun GZ, Guo L, Wang J, et al. Association between hyperuricemia and atrial fibrillation in rural China: a cross-sectional study. BMC Cardiovasc Disord. 2015;15:98.\u003c/li\u003e\n\u003cli\u003eChung MK, Martin DO, Sprecher D, et al. C-reactive protein elevation in patients with atrial arrhythmias: inflammatory mechanisms and persistence of atrial fibrillation. Circulation. 2001;104(24):2886-2891.\u003c/li\u003e\n\u003cli\u003eDing M, Wennberg A, Gigante B, et al. Lipid levels in midlife and risk of atrial fibrillation over 3 decades-Experience from the Swedish AMORIS cohort: A cohort study. PLoS Med. 2022;19(8):e1004044.\u003c/li\u003e\n\u003cli\u003eShitole SG, Heckbert SR, Marcus GM, et al. Assessment of Inflammatory Biomarkers and Incident Atrial Fibrillation in Older Adults. J Am Heart Assoc. 2024;13(24):e035710.\u003c/li\u003e\n\u003cli\u003eSons\u0026ouml;z MR, Demirtaş İ, Canbolat O, et al High-density lipoprotein cholesterol to c-reactive protein ratio predicts atrial fibrillation recurrence after electrical cardioversion. Lipids. 2024.\u003c/li\u003e\n\u003cli\u003eZhou X, Dudley SC Jr. Evidence for Inflammation as a Driver of Atrial Fibrillation. Front Cardiovasc Med. 2020;7:62.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nonvalvular atrial fibrillation, Left atrial thrombus, Spontaneous echo contrast, Residual cholesterol inflammatory index, Transesophageal echocardiography","lastPublishedDoi":"10.21203/rs.3.rs-7452942/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7452942/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCurrently, clinical guidelines are controversial regarding anticoagulation in patients with nonvalvular atrial fibrillation (NVAF) with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores (0\u0026ndash;1 in men and 1\u0026ndash;2 in women). Although these patients have low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores, they are still at risk for left atrial thrombus (LAT) or spontaneous echo contrast (SEC) and further thromboembolism. Studies have shown that residual cholesterol inflammatory index (RCII) can assess both residual cholesterol and low-grade inflammation and is associated with thromboembolism, but the relationship between RCII and LAT/SEC in patients with NVAF has not been clear. Therefore, this study aimed to evaluate the predictive power of RCII for the occurrence of LAT/SEC in NVAF patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAll patients included in the study underwent transesophageal echocardiography (TEE). According to the results of TEE, the patients were divided into the LAT/SEC group and non-LAT /SEC group. The risk factors of LAT/SEC were analyzed by binary logistic regression. The correlation factors were combined with the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores to develop a new prediction model for LAT/SEC, and the predictive efficacy of each model for LAT/SEC was further evaluated by using receiver operating characteristic (ROC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 967 patients with NVAF were included in the study. The RCII level in the LAT/SEC group was significantly higher than that in the non-LAT /SEC group. Increased RCII levels and increased left atrial diameter (LAD) were independent risk factors for the development of LAT/SEC. The incidence of LAT/SEC was higher in the highest quartile array of RCII (\u0026gt;\u0026thinsp;8.81) and LAD (\u0026gt;\u0026thinsp;39mm) than in the corresponding lowest quartile array. The CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores combined with RCII and LAD have good predictive power for LAT/SEC.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eFor NVAF patients with low CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores, increased RCII levels and enlarged LAD are risk factors for LAT/SEC. The CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores combined with RCII and LAD significantly improved the predictive power of LAT/SEC.\u003c/p\u003e","manuscriptTitle":"Predictive value of residual cholesterol inflammatory index for left atrial thrombus or spontaneous echo contrast in patients with nonvalvular atrial fibrillation with low CHA 2 DS 2 -VASc scores","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-19 16:51:52","doi":"10.21203/rs.3.rs-7452942/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8bdc7c34-fe2a-41d9-be9a-9f9c71797222","owner":[],"postedDate":"October 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T09:13:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-19 16:51:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7452942","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7452942","identity":"rs-7452942","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}